Robotic Manipulator State Estimation using Optimized Extended Kalman Filter

Abstract

This paper presents a novel application of Biogeography-Based Optimization (BBO) to optimize the extended Kalman filter (EKF) in order to achieve high performance estimation of states. The parameters to be optimized in an off-line manner are the covariance matrices of state and measurement noises Q and R, respectively. The optimal values of the above covariance matrices are injected into EKF in an on-line manner to estimate states. The suggested approach is demonstrated using a computer simulation of two-link manipulator. Finally, simulations and comparison with particle swarm optimization (PSO) show the effectiveness of proposed method, and exhibit a more superior performance than its conventional counterpart. Index Terms—Biogeography-based optimization, particle swarm optimization, extended Kalman filter, states estimation, two-link manipulator.